---
base_model:
- black-forest-labs/FLUX.1-schnell
tags:
- flux
- diffusers
pipeline_tag: text-to-image
---

# Skittles v2

## Model Summary

**Skittles v2** is a cutting-edge text-to-image generation model, created by merging components from the **FLUX.1 Schnell** architecture. By combining the precision of **FLUX.1 Schnell** with advanced tweaks, **Skittles v2** is designed to offer high-quality image outputs.

- **Type**: Text-to-Image Generation
- **Architecture**: Merged FLUX.1 Schnell with CFG capabilities
- **Output Quality**: Seems to be on par with **FLUX.1 Dev**
- **Performance**: Optimized for both image fidelity (speed is degraded (I'm looking into it))

---

## Features

- **CFG Integration**: Skittles v2 unlocks CFG (Classifier-Free Guidance) capabilities, offering fine-grained control over image generation.
- **High Fidelity**: Produces ultra-realistic and detailed images.
- **Customizable Output**: Supports a wide range of prompts, styles, and configurations.

---

## Model Details

- **Base Model**: FLUX.1 Schnell
- **Merge Approach**: The model was combined using a custom merging strategy, blending FLUX.1 Schnell’s architecture with optimized CFG decoding.
- **Training Paradigm**: Not retrained, but restructured for improved inference performance.
- **Output Size**: Supports resolutions up to 1024x1024 pixels.

---

## Intended Use

### Applications
- Generating ultra-realistic images for creative projects
- Creating concept art, visual prototypes, and artistic renderings
- Exploration of text-to-image synthesis for research or artistic purposes

### Examples

| Prompt | Image Description |
|-------|--------------------|
| "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" | Hyper-detailed astronaut surrounded by lush, muted jungle tones |
| "A futuristic cityscape at sunset, ultra-realistic, cinematic, 4K" | Vibrant, glowing cityscape with dynamic lighting effects |
| "A delicious ceviche cheesecake slice" | Highly detailed and realistic rendition of a culinary masterpiece |

---

## How to Use

```python
from diffusers import DiffusionPipeline

# Load the model
pipe = DiffusionPipeline.from_pretrained("miike-ai/skittles-v2")
pipe.to("cuda")  # Ensure CUDA is available

# Generate an image
prompt = "An ultra-realistic image of a futuristic cityscape."
image = pipe(prompt, guidance_scale=3.5, num_inference_steps=28).images[0]

# Save the result
image.save("generated_image.png")
```

---

## Limitations and Biases

- The model may produce biased or stereotypical outputs based on the provided text prompts.
- Outputs are deterministic but rely heavily on the prompt quality. Results may vary with ambiguous descriptions.
- The model is not trained to handle NSFW content or harmful prompts.

---

## Acknowledgments

- Built on top of **FLUX.1 Schnell** by Black Forest Labs
- Contributions from **miike-ai**
- Integrated with Hugging Face Diffusers for seamless inference

---

## Citation

If you use **Skittles v2** in your work, please cite:

```bibtex
@misc{miike2024skittlesv2,
  title={Skittles v2: A Merged Text-to-Image Generation Model},
  author={miike-ai},
  year={2024},
  url={https://huggingface.co/miike-ai/skittles-v2},
}
```